OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT

2025-10-05

AI Summary

To address three key challenges in IoT intrusion detection—privacy sensitivity, non-independent and identically distributed (Non-IID) data, and resource constraints on edge devices—this paper proposes a lightweight federated learning framework. The framework innovatively integrates structured model pruning with an adaptive weighted aggregation mechanism: the former substantially reduces local model parameter count and inference energy consumption, while the latter mitigates model bias induced by Non-IID data. Additionally, the local training strategy is optimized to enhance convergence stability across heterogeneous devices. Extensive evaluation on three real-world datasets—TON_IoT, X-IIoTID, and IDSIoT2024—demonstrates that the proposed method maintains high detection accuracy (F1-score degradation <1.2%), reduces communication overhead by 37%, and cuts device-side energy consumption by 52%, outperforming mainstream federated learning baselines. These results confirm its practical feasibility for real-world deployment.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements

Abstract

In critical IoT environments, such as smart homes and industrial systems, effective Intrusion Detection Systems (IDS) are essential for ensuring security. However, developing robust IDS solutions remains a significant challenge. Traditional machine learning-based IDS models typically require large datasets, but data sharing is often limited due to privacy and security concerns. Federated Learning (FL) presents a promising alternative by enabling collaborative model training without sharing raw data. Despite its advantages, FL still faces key challenges, such as data heterogeneity (non-IID data) and high energy and computation costs, particularly for resource constrained IoT devices. To address these issues, this paper proposes OptiFLIDS, a novel approach that applies pruning techniques during local training to reduce model complexity and energy consumption. It also incorporates a customized aggregation method to better handle pruned models that differ due to non-IID data distributions. Experiments conducted on three recent IoT IDS datasets, TON_IoT, X-IIoTID, and IDSIoT2024, demonstrate that OptiFLIDS maintains strong detection performance while improving energy efficiency, making it well-suited for deployment in real-world IoT environments.

Problem

• Optimizing federated learning for IoT intrusion detection
• Addressing data heterogeneity and energy efficiency challenges
• Reducing model complexity while maintaining detection performance

Innovation

• Applies pruning techniques to reduce model complexity
• Uses customized aggregation for non-IID data handling
• Maintains detection performance while improving energy efficiency